import cv2
import numpy as np
import os
from matplotlib import pyplot as plt

INPUT_IMAGE = "data/selfie.jpg"
OUT_DIR = "results_ws4"
NAMA_MAHASISWA = "Fathan Andi Kartagama"

os.makedirs(OUT_DIR, exist_ok=True)

img_bgr = cv2.imread(INPUT_IMAGE)
if img_bgr is None:
    raise FileNotFoundError(f"Gambar tidak ditemukan: {INPUT_IMAGE}")

print(f"Ukuran gambar asli: {img_bgr.shape[1]}x{img_bgr.shape[0]} (W x H)")

Ukuran gambar asli: 1836x3264 (W x H)

# Cropping Manual – Wajah

print("[1] Pilih ROI Wajah (usahakan kotak di area wajah).")
print("    Cara: drag mouse → ENTER/SPACE untuk OK, atau C untuk batal.")
roi_face = cv2.selectROI("Pilih ROI Wajah", img_bgr, showCrosshair=True)
cv2.destroyWindow("Pilih ROI Wajah")

x_f, y_f, w_f, h_f = roi_face

# Biar benar-benar persegi, pakai sisi terkecil
side = min(w_f, h_f)
cx = x_f + w_f // 2
cy = y_f + h_f // 2

x0 = max(cx - side // 2, 0)
y0 = max(cy - side // 2, 0)
x1 = x0 + side
y1 = y0 + side

face_square = img_bgr[y0:y1, x0:x1]

# Cropping Manual – Background 

print("[2] Pilih ROI Background (latar belakang, bentuk persegi panjang).")
print("    Cara: drag mouse → ENTER/SPACE untuk OK, atau C untuk batal.")
roi_bg = cv2.selectROI("Pilih ROI Background", img_bgr, showCrosshair=True)
cv2.destroyWindow("Pilih ROI Background")

x_b, y_b, w_b, h_b = roi_bg
bg_rect = img_bgr[y_b:y_b + h_b, x_b:x_b + w_b]

[1] Pilih ROI Wajah (usahakan kotak di area wajah).
    Cara: drag mouse → ENTER/SPACE untuk OK, atau C untuk batal.
Select a ROI and then press SPACE or ENTER button!
Cancel the selection process by pressing c button!

qt.qpa.plugin: Could not find the Qt platform plugin "wayland" in "/home/pataangg/Developer/Mulmed/.venv/lib/python3.12/site-packages/cv2/qt/plugins"
QApplication: invalid style override 'kvantum' passed, ignoring it.
	Available styles: Windows, Fusion

[2] Pilih ROI Background (latar belakang, bentuk persegi panjang).
    Cara: drag mouse → ENTER/SPACE untuk OK, atau C untuk batal.
Select a ROI and then press SPACE or ENTER button!
Cancel the selection process by pressing c button!

TARGET_SIZE = (920, 920)

face_resized = cv2.resize(face_square, TARGET_SIZE, interpolation=cv2.INTER_AREA)
bg_resized = cv2.resize(bg_rect, TARGET_SIZE, interpolation=cv2.INTER_AREA)

# Add Annotation (nama) di atas kepala

annotated = face_resized.copy()

font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 2.0
thickness = 4
color = (0, 255, 255)  # kuning

(text_w, text_h), baseline = cv2.getTextSize(NAMA_MAHASISWA, font, font_scale, thickness)
x_text = (annotated.shape[1] - text_w) // 2
y_text = text_h + 40  # sedikit turun dari atas

cv2.putText(
    annotated,
    NAMA_MAHASISWA,
    (x_text, y_text),
    font,
    font_scale,
    color,
    thickness,
    lineType=cv2.LINE_AA
)

array([[[210, 227, 230],
        [208, 225, 228],
        [207, 224, 227],
        ...,
        [198, 211, 209],
        [194, 206, 206],
        [192, 204, 204]],

       [[210, 227, 230],
        [207, 224, 227],
        [206, 223, 226],
        ...,
        [200, 212, 210],
        [200, 211, 210],
        [195, 207, 206]],

       [[209, 226, 229],
        [208, 225, 228],
        [209, 226, 229],
        ...,
        [193, 204, 202],
        [193, 204, 202],
        [194, 205, 203]],

       ...,

       [[198, 221, 229],
        [197, 219, 227],
        [200, 221, 229],
        ...,
        [ 62, 120, 172],
        [ 58, 115, 169],
        [ 53, 111, 166]],

       [[197, 220, 228],
        [196, 218, 226],
        [201, 222, 230],
        ...,
        [ 62, 118, 171],
        [ 59, 116, 170],
        [ 56, 112, 167]],

       [[199, 222, 230],
        [200, 222, 230],
        [200, 221, 229],
        ...,
        [ 59, 113, 166],
        [ 63, 118, 172],
        [ 61, 117, 172]]], shape=(920, 920, 3), dtype=uint8)

# Konversi gambar ke format yang berbeda
gray = cv2.cvtColor(annotated, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(annotated, cv2.COLOR_BGR2HSV)

# Plot all resized face images
plt.figure(figsize=(15, 5))

plt.subplot(1, 3, 1)
plt.title("Resized Face Crop")
plt.imshow(cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB))
plt.axis('off')

plt.subplot(1, 3, 2)
plt.title("Grayscale Face Crop")
plt.imshow(gray, cmap='gray')
plt.axis('off')

plt.subplot(1, 3, 3)
plt.title("HSV Face Crop")
plt.imshow(hsv)
plt.axis('off')

plt.show()

# Simpan hasil
cv2.imwrite(os.path.join(OUT_DIR, "soal1_face_square_raw.png"), face_square)
cv2.imwrite(os.path.join(OUT_DIR, "soal1_face_resized_920x920.png"), face_resized)
cv2.imwrite(os.path.join(OUT_DIR, "soal1_bg_rect_resized_920x920.png"), bg_resized)

cv2.imwrite(os.path.join(OUT_DIR, "soal1_face_annotated_bgr.png"), annotated)
cv2.imwrite(os.path.join(OUT_DIR, "soal1_face_gray.png"), gray)
cv2.imwrite(os.path.join(OUT_DIR, "soal1_face_hsv.png"), hsv)

print("Semua hasil sudah disimpan di folder:", OUT_DIR)

Semua hasil sudah disimpan di folder: results_ws4

# Konfigurasi untuk Soal 2

IN_DIR = "results_ws4"
OUT_DIR = "results_ws4"
os.makedirs(OUT_DIR, exist_ok=True)

# Pakai gambar hasil crop dari Soal 1
INPUT_CROP = os.path.join(IN_DIR, "soal1_face_resized_920x920.png")
OUTPUT_MODIFIED = os.path.join(OUT_DIR, "soal2_rgb_modified.png")

# Load gambar dam Konversi ke RGB
img_bgr = cv2.imread(INPUT_CROP)
if img_bgr is None:
    raise FileNotFoundError(f"Gambar tidak ditemukan: {INPUT_CROP}")
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

# Split channel R, G, B
r, g, b = cv2.split(img_rgb)

# Konversi ke tipe lebih besar dulu biar aman dari overflow/underflow
r_16 = r.astype(np.int16)
g_16 = g.astype(np.int16)
b_16 = b.astype(np.int16)

# Manipulasi channel
r_mod = np.clip(r_16 + 50, 0, 255).astype(np.uint8)
b_mod = np.clip(b_16 - 30, 0, 255).astype(np.uint8)
g_mod = g  # tidak diubah

img_rgb_mod = cv2.merge((r_mod, g_mod, b_mod))

# Simpan sebagai PNG (konversi dulu ke BGR untuk cv2.imwrite)
img_bgr_mod = cv2.cvtColor(img_rgb_mod, cv2.COLOR_RGB2BGR)
cv2.imwrite(OUTPUT_MODIFIED, img_bgr_mod)
print("Gambar hasil modifikasi disimpan di:", OUTPUT_MODIFIED)

# Tampilkan gambar asli dan hasil modifikasi
plt.figure(figsize=(10, 5))

plt.subplot(1, 2, 1)
plt.imshow(img_rgb)
plt.title("Asli (RGB)")
plt.axis("off")

plt.subplot(1, 2, 2)
plt.imshow(img_rgb_mod)
plt.title("Setelah Modifikasi RGB")
plt.axis("off")

plt.tight_layout()
plt.show()

Gambar hasil modifikasi disimpan di: results_ws4/soal2_rgb_modified.png

fig, axes = plt.subplots(2, 3, figsize=(15, 8))

channels_orig = [r, g, b]
channels_mod = [r_mod, g_mod, b_mod]
colors = ['red', 'green', 'blue']
names = ['Red', 'Green', 'Blue']

# Histogram gambar asli
for i, (ch, c, name) in enumerate(zip(channels_orig, colors, names)):
    axes[0, i].hist(ch.flatten(), bins=256, range=(0, 255), color=c, alpha=0.8)
    axes[0, i].set_title(f"Histogram {name} (Asli)")
    axes[0, i].set_xlim([0, 255])

# Histogram gambar modifikasi
for i, (ch, c, name) in enumerate(zip(channels_mod, colors, names)):
    axes[1, i].hist(ch.flatten(), bins=256, range=(0, 255), color=c, alpha=0.8)
    axes[1, i].set_title(f"Histogram {name} (Modifikasi)")
    axes[1, i].set_xlim([0, 255])

plt.tight_layout()
plt.show()

import cv2
import numpy as np
import matplotlib.pyplot as plt
import os

# Path langsung ke file (karena sudah di folder Worksheet-4)
IMG_PATH = os.path.join("data", "koin.jpg")

if not os.path.exists(IMG_PATH):
    raise FileNotFoundError(f"Gambar tidak ditemukan: {IMG_PATH}")

img = cv2.imread(IMG_PATH)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Gaussian Blur untuk mengurangi noise
blur = cv2.GaussianBlur(img_rgb, (5, 5), 0)

# Konversi ke grayscale
gray_blur = cv2.cvtColor(blur, cv2.COLOR_RGB2GRAY)

# Thresholding 
threshold_value = 180
_, thresh_img = cv2.threshold(gray_blur, threshold_value, 255, cv2.THRESH_BINARY)

# Find contours in the thresholded image
contours_thresh, _ = cv2.findContours(thresh_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

img_with_bbox = img_rgb.copy() 

if contours_thresh:
    # Find the largest contour (assuming it's your main object)
    largest_contour = max(contours_thresh, key=cv2.contourArea)
    
    # Get the bounding box coordinates for that contour
    x, y, w, h = cv2.boundingRect(largest_contour)
    
    # Draw the automatically found rectangle
    cv2.rectangle(
        img_with_bbox, 
        (x, y),                  # Top-left corner (automatic)
        (x + w, y + h),          # Bottom-right corner (automatic)
        (0, 255, 0),             # Color (Green)
        3                        # Thickness
    )
else:
    print("No contours were found.")

# Plot Threshold and Bounding Box results
fig_bbox, axs_bbox = plt.subplots(1, 2, figsize=(10, 5))

axs_bbox[0].imshow(thresh_img, cmap='gray')
axs_bbox[0].set_title(f"Threshold Result (Value={threshold_value})", fontsize=12)
axs_bbox[0].axis("off")

axs_bbox[1].imshow(img_with_bbox)
axs_bbox[1].set_title("Automatic Bounding Box", fontsize=12)
axs_bbox[1].axis("off")

plt.tight_layout()
plt.show()

# Filter Blur
blurred = cv2.GaussianBlur(img_rgb, (25, 25), 0)

#Filter Sharpening
kernel_sharp = np.array([
    [0, -1, 0],
    [-1, 5, -1],
    [0, -1, 0]
])
sharpened = cv2.filter2D(img_rgb, -1, kernel_sharp)

plt.figure(figsize=(16, 10))

plt.subplot(2, 3, 1)
plt.imshow(img_rgb)
plt.title("Gambar Asli")
plt.axis("off")

plt.subplot(2, 3, 2)
plt.imshow(edges, cmap='gray')
plt.title("Edge Detection (Canny)")
plt.axis("off")

plt.subplot(2, 3, 3)
plt.imshow(thresh, cmap='gray')
plt.title("Thresholding (Segmentasi Objek)")
plt.axis("off")

plt.subplot(2, 3, 4)
plt.imshow(img_with_bbox)
plt.title("Bounding Box pada Objek")
plt.axis("off")

plt.subplot(2, 3, 5)
plt.imshow(blurred)
plt.title("Filter Blur (Gaussian)")
plt.axis("off")

plt.subplot(2, 3, 6)
plt.imshow(sharpened)
plt.title("Filter Sharpening")
plt.axis("off")

plt.tight_layout()
plt.show()

# Pastikan folder results_ws4 ada
OUT_DIR_S3 = "results_ws4"
os.makedirs(OUT_DIR_S3, exist_ok=True)

# Simpan gambar asli (konversi ke BGR untuk cv2.imwrite)
cv2.imwrite(os.path.join(OUT_DIR_S3, "soal3_original.png"), cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR))

# Simpan edge detection (Canny) - langsung dari gray, perlu dibuat dulu
edges = cv2.Canny(gray, 100, 200)
cv2.imwrite(os.path.join(OUT_DIR_S3, "soal3_canny_edges.png"), edges)

# Simpan threshold (sudah ada sebagai thresh_img)
cv2.imwrite(os.path.join(OUT_DIR_S3, "soal3_threshold.png"), thresh_img)

# Simpan bounding box
cv2.imwrite(os.path.join(OUT_DIR_S3, "soal3_bounding_box.png"), cv2.cvtColor(img_with_bbox, cv2.COLOR_RGB2BGR))

# Simpan blur
cv2.imwrite(os.path.join(OUT_DIR_S3, "soal3_blur.png"), cv2.cvtColor(blurred, cv2.COLOR_RGB2BGR))

# Simpan sharpening
cv2.imwrite(os.path.join(OUT_DIR_S3, "soal3_sharpening.png"), cv2.cvtColor(sharpened, cv2.COLOR_RGB2BGR))

print(f"Semua hasil Soal 3 sudah disimpan di folder: {OUT_DIR_S3}")
print("File yang disimpan:")
print("  - soal3_original.png")
print("  - soal3_canny_edges.png")
print("  - soal3_threshold.png")
print("  - soal3_bounding_box.png")
print("  - soal3_blur.png")
print("  - soal3_sharpening.png")

Semua hasil Soal 3 sudah disimpan di folder: results_ws4
File yang disimpan:
  - soal3_original.png
  - soal3_canny_edges.png
  - soal3_threshold.png
  - soal3_bounding_box.png
  - soal3_blur.png
  - soal3_sharpening.png

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import mediapipe as mp

# Konfigurasi path
DATA_DIR = os.path.join("data")
OUT_DIR = "results_ws4"
os.makedirs(OUT_DIR, exist_ok=True)

SELFIE_PATH = os.path.join(DATA_DIR, "selfie.jpg")
FILTER_PATH = os.path.join(DATA_DIR, "thug.png")  # PNG dengan alpha

# Load gambar selfie & filter

img_bgr = cv2.imread(SELFIE_PATH)
if img_bgr is None:
    raise FileNotFoundError(f"Gambar tidak ditemukan: {SELFIE_PATH}")

h, w = img_bgr.shape[:2]
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

# Filter PNG (harus 4 channel: BGRA)
filter_png = cv2.imread(FILTER_PATH, cv2.IMREAD_UNCHANGED)
if filter_png is None or filter_png.shape[2] != 4:
    raise ValueError("thug.png harus berupa PNG dengan alpha channel (4 channel).")

mp_face_mesh = mp.solutions.face_mesh

with mp_face_mesh.FaceMesh(
    static_image_mode=True,
    max_num_faces=1,
    refine_landmarks=True,
    min_detection_confidence=0.5
) as face_mesh:
    results = face_mesh.process(img_rgb)

if not results.multi_face_landmarks:
    raise RuntimeError("Tidak ada wajah terdeteksi pada selfie.jpg")

face_landmarks = results.multi_face_landmarks[0]

# Konversi landmark ke koordinat piksel
landmarks = []
for lm in face_landmarks.landmark:
    x_px = int(lm.x * w)
    y_px = int(lm.y * h)
    landmarks.append((x_px, y_px))

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1763312484.884762   51260 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5
I0000 00:00:1763312484.918577   54827 gl_context.cc:357] GL version: 3.2 (OpenGL ES 3.2 Mesa 25.2.7-arch1.1), renderer: Mesa Intel(R) UHD Graphics (TGL GT2)
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
W0000 00:00:1763312484.924035   54818 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.
W0000 00:00:1763312484.968560   54817 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.
/home/pataangg/Developer/Mulmed/.venv/lib/python3.12/site-packages/google/protobuf/symbol_database.py:55: UserWarning: SymbolDatabase.GetPrototype() is deprecated. Please use message_factory.GetMessageClass() instead. SymbolDatabase.GetPrototype() will be removed soon.
  warnings.warn('SymbolDatabase.GetPrototype() is deprecated. Please '

left_eye = landmarks[33]   # sudut luar mata kiri
right_eye = landmarks[263] # sudut luar mata kanan

cx = int((left_eye[0] + right_eye[0]) / 2)
cy = int((left_eye[1] + right_eye[1]) / 2)

eye_distance = np.linalg.norm(np.array(left_eye) - np.array(right_eye))

SCALE_W = 2.3  # diperbesar dari 2.2 agar lebih lebar
overlay_width = int(SCALE_W * eye_distance)

fh, fw = filter_png.shape[:2]
aspect_ratio = fh / fw
overlay_height = int(overlay_width * aspect_ratio)

# Pusatkan di garis mata dengan offset vertikal dan horizontal
x1 = int(cx - overlay_width / 2)
y1 = int(cy - overlay_height / 2)

# Offset horizontal untuk menggeser ke kiri (negatif = kiri, positif = kanan)
HORIZ_OFFSET = -0.05  # sesuaikan nilai ini (coba -0.03 hingga -0.1)
x1 = x1 + int(HORIZ_OFFSET * overlay_width)

# Offset vertikal untuk menurunkan posisi kacamata
VERT_OFFSET = 0.15  # positif = turun, bisa disesuaikan (coba 0.1 - 0.3)
y1 = y1 + int(VERT_OFFSET * overlay_height)

x2 = x1 + overlay_width
y2 = y1 + overlay_height

# Clipping jika keluar frame

x1_clip = max(0, x1)
y1_clip = max(0, y1)
x2_clip = min(w, x2)
y2_clip = min(h, y2)

# Update ukuran overlay sesuai area yang tersisa
overlay_width_clip = x2_clip - x1_clip
overlay_height_clip = y2_clip - y1_clip

if overlay_width_clip <= 0 or overlay_height_clip <= 0:
    raise RuntimeError("Overlay di luar area gambar, sesuaikan skala / posisi.")

# Resize filter ke ukuran yang sudah di-clip
filter_resized = cv2.resize(filter_png, (overlay_width_clip, overlay_height_clip), interpolation=cv2.INTER_AREA)

# Pisahkan BGR dan alpha
filter_bgr = filter_resized[:, :, :3]
filter_alpha = filter_resized[:, :, 3] / 255.0  # ke [0,1]

# Alpha blending
output = img_rgb.copy()

roi = output[y1_clip:y2_clip, x1_clip:x2_clip]

# pastikan shape sama
if roi.shape[:2] != filter_bgr.shape[:2]:
    raise RuntimeError("Ukuran ROI dan filter tidak cocok setelah resize.")

alpha = filter_alpha[..., np.newaxis]  # (H, W, 1)

roi_blended = (alpha * filter_bgr + (1 - alpha) * roi).astype(np.uint8)

output[y1_clip:y2_clip, x1_clip:x2_clip] = roi_blended

out_bgr = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
out_path = os.path.join(OUT_DIR, "soal4_selfie_thug_filter.png")
cv2.imwrite(out_path, out_bgr)
print("Hasil filter disimpan di:", out_path)

plt.figure(figsize=(12, 6))

plt.subplot(1, 2, 1)
plt.imshow(img_rgb)
plt.title("Gambar Asli (Selfie)")
plt.axis("off")

plt.subplot(1, 2, 2)
plt.imshow(output)
plt.title("Selfie + Thug Filter (Alpha Blending)")
plt.axis("off")

plt.tight_layout()
plt.show()

Hasil filter disimpan di: results_ws4/soal4_selfie_thug_filter.png

import cv2
import numpy as np
import matplotlib.pyplot as plt
import os

IMG_PATH = os.path.join( "data","kertas.jpg")

img = cv2.imread(IMG_PATH)
if img is None:
    raise FileNotFoundError(f"Gambar tidak ditemukan: {IMG_PATH}")

img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

print("Pilih 4 titik sudut kertas: urutkan searah jarum jam atau counter-clockwise.")
print("Klik 4 sudut → tekan ENTER.")

points = []

def click_event(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        points.append((x, y))
        cv2.circle(tmp, (x, y), 6, (0, 0, 255), -1)
        cv2.imshow("Pilih 4 Titik", tmp)

# Resize gambar untuk display agar lebih kecil
scale_factor = 0.3  # sesuaikan jika perlu (0.2 = 20% dari ukuran asli)
display_height = int(img_rgb.shape[0] * scale_factor)
display_width = int(img_rgb.shape[1] * scale_factor)
tmp = cv2.resize(img_rgb, (display_width, display_height))

cv2.imshow("Pilih 4 Titik", tmp)
cv2.setMouseCallback("Pilih 4 Titik", click_event)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Scale kembali koordinat ke ukuran asli
pts_src = np.array(points, dtype=np.float32) / scale_factor

Pilih 4 titik sudut kertas: urutkan searah jarum jam atau counter-clockwise.
Klik 4 sudut → tekan ENTER.

# Estimasi: lebar = jarak antara titik atas, tinggi = jarak antara titik kiri
width = int(np.linalg.norm(pts_src[0] - pts_src[1]))
height = int(np.linalg.norm(pts_src[0] - pts_src[3]))

pts_dst = np.array([
    [0, 0],
    [width, 0],
    [width, height],
    [0, height]
], dtype=np.float32)

# Hitung homografi & warp perspective

H, _ = cv2.findHomography(pts_src, pts_dst)
warped = cv2.warpPerspective(img_rgb, H, (width, height))

# grayscale setelah warp
warped_gray = cv2.cvtColor(warped, cv2.COLOR_RGB2GRAY)

thresh = cv2.adaptiveThreshold(
    warped_gray, 255,
    cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
    cv2.THRESH_BINARY,
    25, 10
)

# Simpan hasil terlebih dahulu
OUT_DIR = "results_ws4"
os.makedirs(OUT_DIR, exist_ok=True)

cv2.imwrite(os.path.join(OUT_DIR, "soal5_original.png"), cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(OUT_DIR, "soal5_grayscale.png"), gray)
cv2.imwrite(os.path.join(OUT_DIR, "soal5_warped.png"), cv2.cvtColor(warped, cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(OUT_DIR, "soal5_threshold.png"), thresh)

# Plot dengan layout yang lebih rapi
fig = plt.figure(figsize=(16, 10))

# Gambar Asli
plt.subplot(2, 2, 1)
plt.imshow(img_rgb)
plt.title("1. Gambar Asli", fontsize=14, fontweight='bold', pad=10)
plt.axis("off")

# Grayscale
plt.subplot(2, 2, 2)
plt.imshow(gray, cmap="gray")
plt.title("2. Konversi Grayscale", fontsize=14, fontweight='bold', pad=10)
plt.axis("off")

# Koreksi Perspektif
plt.subplot(2, 2, 3)
plt.imshow(warped)
plt.title("3. Koreksi Perspektif (Warp)", fontsize=14, fontweight='bold', pad=10)
plt.axis("off")

# Thresholding Adaptif
plt.subplot(2, 2, 4)
plt.imshow(thresh, cmap="gray")
plt.title("4. Thresholding Adaptif", fontsize=14, fontweight='bold', pad=10)
plt.axis("off")

plt.suptitle("Pipeline Preprocessing Citra: Koreksi Perspektif & Peningkatan Kualitas", 
             fontsize=16, fontweight='bold', y=0.98)
plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.show()

print(f"\nSemua hasil Soal 5 telah disimpan di folder: {OUT_DIR}")
print("File yang disimpan:")
print("  - soal5_original.png")
print("  - soal5_grayscale.png")
print("  - soal5_warped.png")
print("  - soal5_threshold.png")

Semua hasil Soal 5 telah disimpan di folder: results_ws4
File yang disimpan:
  - soal5_original.png
  - soal5_grayscale.png
  - soal5_warped.png
  - soal5_threshold.png

Tugas Analisis Multimedia: Image (Citra Digital)¶

Deskripsi Tugas¶

Soal 1 — Cropping dan Konversi Warna¶

Konfigurasi Awal¶

Load Gambar¶

Cropping Manual - Wajah & Cropping Manual Background¶

Resize 920x920¶

Konversi dari BGR (OpenCV) ke Grayscale & HSV¶

Print Image dan Save ke Direktori¶

PENJELASAN¶

Soal 2 — Manipulasi Channel Warna RGB¶

Konfigurasi dan Load Gambar¶

Split Channel R, G, B dan Manipulasi Channel¶

Gabungkan menjadi gambar RGB kembali & Print Image¶

Histogram Perchannel Asli dan Modified¶

PENJELASAN¶

Soal 3 — Deteksi Tepi dan Filter Citra¶

Load Gambar¶

Edge Detection, Thresholding (Segmentasi), dan Bounding Box Otomatis¶

Filter Blur dan Sharpening¶

Plot Semua Gambar¶

PENJELASAN¶

Soal 4 — Deteksi Wajah dan Filter Digital Kreatif¶

Load Gambar Selfie dan Filter Thug¶

Deteksi landmark Wajah dengan Menggunakan Mediapipe Facemesh¶

Print Image¶

PENJELASAN¶

Soal 5 — Perspektif dan Peningkatan Kualitas Citra¶

Pilih Titik secara manual¶

Tentukan ukuran output hasil koreksi perspektif¶

Plot Gambar¶

PENJELASAN¶

LAMPIRAN¶